How Tourist Group Books Hotels Meeting the Majority Affective Expectations: A Group Selection Frame with Kansei Text Mining and Consensus Coordinating

2023 | Group Decision and Negotiation | Citations: 0

Authors: Chang, Jia-Li; Li, Hui; Wu, Jian

Abstract: The decision on which hotel to book is a high-cost process for individuals and e ... Expand

Abstract: The decision on which hotel to book is a high-cost process for individuals and even more for groups due to the information explosion on online travel agent platforms. To assist group selecting the optimal hotel that meets the majority affective expectations, this study proposes a group hotel selection frame for booking with Kansei text mining and consensus coordinating. Firstly, to quantify the tourists’ affective needs for hotels, a Kansei-related dictionary towards hotel domain is constructed by Kansei text mining. Secondly, the bilattice-based Kansei score of hotels is defined and measured to represent hotel affective information scientifically. Then, a group consensus model with minimum adjustment cost is introduced to coordinate consistency of individual preferences in a tourist group. Moreover, a group hotel ranking based on group affective preference is interactively generated to meet the group personalized demands. Finally, a case study on Trip.com is conducted to demonstrate the proposed method. Collapse

Topics: knowledge representation online review personality website

Methods: natural language processing word2vec computational algorithm sentiment analysis case study

Interpretable machine learning-based approach for customer segmentation for new product development from online product reviews

2023 | International Journal of Information Management | Citations: 0

Authors: Joung, Junegak; Kim, Harrison

Abstract: For new product development, previous segmentation methods based on demographic, ... Expand

Abstract: For new product development, previous segmentation methods based on demographic, psychographic, and purchase behavior information cannot identify a group of customers with unsatisfied needs. Moreover, seg­ mentation is limited to sales promotions in marketing. Although needs-based segmentation considering customer sentiments on product features can be conducted to develop a new product concept, it cannot identify com­ monalities among customers owing to their diverse preferences. Therefore, this paper proposes an interpretable machine learning-based approach for customer segmentation for new product development based on the importance of product features from online product reviews. The technical challenges of determining the importance of product features in each review are identifying and interpreting the nonlinear relations between satisfaction with product features and overall customer satisfaction. In this study, interpretable machine learning is used to identify these nonlinear relations with high performance and transparency. A case study on a wearable device is conducted to validate the proposed approach. Customer segmentation using the proposed approach based on importance is compared with that employing a previous approach based on sentiments. The results show that the proposed approach presents a higher clustering performance than the previous approach and offers opportunities to identify new product concepts. Collapse

Topics: online review shapley additive explanation customer satisfaction new product development Python

Methods: cluster analysis machine learning sentiment analysis case study word2vec

Theories: game theory

Diffusion of real versus misinformation during a crisis event: A big data-driven approach

2023 | International Journal of Information Management | Citations: 0

Authors: King, Kelvin K.; Wang, Bin

Abstract: Misinformation has captured the interest of academia in recent years with severa ... Expand

Abstract: Misinformation has captured the interest of academia in recent years with several studies looking at the topic broadly with inconsistent results. In this research, we attempt to bridge the gap in the literature by examining the impacts of user-, time-, and content-based characteristics that affect the virality of real versus misinformation during a crisis event. Using a big data-driven approach, we collected over 42 million tweets during Hurricane Harvey and obtained 3589 original verified real or false tweets by cross-checking with fact-checking websites and a relevant federal agency. Our results show that virality is higher for misinformation, novel tweets, and tweets with negative sentiment or lower lexical density. In addition, we reveal the opposite impacts of sentiment on the virality of real news versus misinformation. We also find that tweets on the environment are less likely to go viral than the baseline religious news, while real social news tweets are more likely to go viral than misin­ formation on social news. Collapse

Topics: Twitter misinformation social media hurricane big data

Methods: machine learning computational algorithm natural language processing decision tree classification gradient boosting

Learning Personalized Privacy Preference From Public Data

2023 | International Conference on Information Systems | Citations: 0

Authors: Wang, Wen; Li, Beibei

Abstract: Understanding consumers' privacy preferences is crucial for firms and policymak ... Expand

Abstract: Understanding consumers' privacy preferences is crucial for firms and policymakers to establish trust and encourage innovation and competition. With the widespread use of digital technologies, individuals generate and share vast amounts of data about them selves in the public domain. Even without knowing a person's private information, the psychosocial traits revealed in public data can provide valuable insights into their pri vacy preferences. In this study, we aim to predict personalized privacy preferences us ing social media posts. Our prediction model shows that psychosocial traits such as per sonality, lifestyles, riskpreference, economic thinking, emotions, etc extracted from posts provide significantly greater predictive power than demographic characteristics. Fur thermore, we demonstrate the practical value and impact of our model for business and society through a simulation analysis. Our tool can help platforms and policymakers es timate the impact of privacy policies and prevent potential harms such as discrimination. Collapse

Topics: privacy social media Twitter shapley additive explanation information privacy concern

Methods: BERT machine learning experiment computational algorithm topic model

Theories: game theory

Getting Personal: A Deep Learning Artifact for Text-Based Measurement of Personality

2023 | Information Systems Research | Citations: 0

Authors: Yang, Kai; Lau, Raymond Y. K.; Abbasi, Ahmed

Abstract: Analysts, managers, and policymakers are interested in predictive analytics capa ... Expand

Abstract: Analysts, managers, and policymakers are interested in predictive analytics capable of offering better foresight. It is generally accepted that in forecasting scenarios involving organizational policies or consumer decision making, personal characteristics, including personality, may be an important predictor of downstream outcomes. The inclusion of personality features in forecasting models has been hindered by the fact that traditional measurement mechanisms are often infeasible. Text-based personality detection has garnered attention because of the public availability of digital textual traces. However, the text machine learning space has bifurcated into two branches: feature-based methods relying on manually crafted human intuition, or deep learning language models that leverage big data and compute, the main commonality being that neither branch generates accurate personality assessments, thereby making personality measures infeasible for downstream forecasting applications. In this study, we propose DeepPerson, a design artifact for text-based personality detection that bridges these two branches by leveraging concepts from relevant psycholinguistic theories in conjunction with advanced deep learning strategies. DeepPerson incorporates novel transfer learning and hierarchical attention network methods that use psychological concepts and data augmentation in conjunction with person-level linguistic information. We evaluate the utility of the proposed artifact using an extensive design evaluation on three personality data sets in comparison with state-of-the-art methods proposed in academia and industry. DeepPerson can improve detection of personality dimensions by 10–20 percentage points relative to the best comparison methods. Using case studies in the finance and health domains, we show that more accurate text-based personality detection can translate into significant improvements in downstream applications such as forecasting future firm performance or predicting pandemic infection rates. Our findings have important implications for research at the intersection of design and data science, and practical implications for managers focused on enabling, producing, or consuming predictive analytics.History: Olivia Sheng, Senior Editor; Huimin Zhao, Associate Editor.Funding: This work was supported by Oracle for Research (NLP for the Greater Good) and the U.S. National Science Foundation’s Division of Information and Intelligent Systems [Grants BDS-1636933 and IIS-1816504]. The work was also partly supported by the Research Grants Council of the Hong Kong Special Administrative Region [Project: CityU 11507219] and the City University of Hong Kong SRG [Project: 7005780].Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2022.1111. Collapse

Topics: personality Twitter social media pandemic Google+

Methods: machine learning BERT design science deep learning computational algorithm

Theories: big five model

A Combined Weighting Based Large Scale Group Decision Making Framework for MOOC Group Recommendation

2023 | Group Decision and Negotiation | Citations: 0

Authors: Zhang, Chonghui; Su, Weihua; Chen, Sichao; Zeng, Shouzhen; Liao, Huchang

Abstract: Massive open online courses (MOOC) are free learning courses based on online pla ... Expand

Abstract: Massive open online courses (MOOC) are free learning courses based on online platforms for higher education, which not only promote the open sharing of learning resources, but also lead to serious information overload. However, there are many courses on MOOCs, and it can be difficult for users to choose courses that match their individual or group preferences. Therefore, a combined weighting based large-scale group decision-making approach is proposed to implement MOOC group recommendations. First, based on the MOOC operation mode, we decompose the course content into three stages, namely pre-class, in-class, and post-class, and then the curriculum-arrangement-movement- performance evaluation framework is constructed. Second, the probabilistic linguistic criteria importance through intercriteria correlation method is employed to obtain the objective weighting of the criterion. Meanwhile, the word embedding model is utilized to vectorize online reviews, and the subjective weighting of the criteria are acquired by calculating the text similarity. The combined weighting then can be obtained by fusing the subjective and objective weighting. Based on this, the PL-MULTIMIIRA approach and Borda rule is employed to rank the alternatives for group recommendation, and an easy-to-use formula for group satisfaction is proposed to evaluate the effect of the proposed method. Furthermore, a case study is conducted to group recommendations for statistical MOOCs. Finally, the robustness and effectiveness of the proposed approach were verified through sensitivity analysis as well as comparative analysis. Collapse

Topics: online review online learning system user-generated content decision making recommender system

Methods: descriptive statistic GloVe cluster analysis trade-off analysis method computational algorithm

Transformer-Based Multi-Task Learning for Crisis Actionability Extraction

2023 | International Conference on Information Systems | Citations: 0

Authors: Zhang, Yuhao; Lo, Siaw Ling; Myint, Phyo Yi Win

Abstract: Social media has become a valuable information source for crisis informatics. W ... Expand

Abstract: Social media has become a valuable information source for crisis informatics. While various methods were proposed to extract relevant information during a crisis, their adoption by field practitioners remains low. In recent fieldwork, actionable information was identified as the primary information need for crisis responders and a key component in bridging the significant gap in existing crisis management tools. In this paper, we proposed a Crisis Actionability Extraction System for filtering, classification, phrase extraction, severity estimation, localization, and aggregation of actionable information altogether. We examined the effectiveness of transformer-based LSTM-CRF architecture in Twitter-related sequence tagging tasks and simultaneously extracted actionable information such as situational details and crisis impact via Multi-Task Learning. We demonstrated the system's practical value in a case study of a real-world crisis and showed its effectiveness in aiding crisis responders with making well-informed decisions, mitigating risks, and navigating the complexities of the crisis. Collapse

Topics: Twitter social media systems design hurricane knowledge base

Methods: BERT case study GloVe machine learning experiment

Know Thy Context: Parsing Contextual Information from User Reviews for Recommendation Purposes

2022 | Information Systems Research | Citations: 1

Authors: Bauman, Konstantin; Tuzhilin, Alexander

Abstract: In this paper, we study an important problem of parsing contextual information f ... Expand

Abstract: In this paper, we study an important problem of parsing contextual information from user reviews for recommendation purposes. First, we study the ways contextual information is expressed in user reviews and obtain novel insights about it. Among other things, we demonstrate that such type of information tends to appear at the beginning of the review, in longer sentences, in the sentences written in the past tense or using gerund form, and in the sentences referring to some points in time. Second, we propose a novel context parsing method for systematically extracting contextual information from user-generated reviews that relies on the insights obtained in our study. We apply the proposed method to three different Yelp applications (restaurants, hotels, and beauty & spas) and demonstrate that it works well and leads to better recommendation performance than the baseline approaches. Our method systematically extracts more comprehensive sets of relevant contextual variables and corresponding phrases than the baselines. Our analysis also shows the importance of the newly discovered contextual information for recommendation purposes. The obtained results and the proposed method can help to get more comprehensive knowledge about contextual variables in a given application that leads to better recommendations. Collapse

Topics: online review recommender system logistics management user experience user-generated content

Methods: word2vec hierarchical clustering machine learning product review experiment

Global reconstruction of language models with linguistic rules – Explainable AI for online consumer reviews

2022 | Electronic Markets | Citations: 0

Authors: Binder, Markus; Heinrich, Bernd; Hopf, Marcus; Schiller, Alexander

Abstract: Analyzing textual data by means of AI models has been recognized as highly relev ... Expand

Abstract: Analyzing textual data by means of AI models has been recognized as highly relevant in information systems research and practice, since a vast amount of data on eCommerce platforms, review portals or social media is given in textual form. Here, language models such as BERT, which are deep learning AI models, constitute a breakthrough and achieve leading-edge results in many applications of text analytics such as sentiment analysis in online consumer reviews. However, these language models are “black boxes”: It is unclear how they arrive at their predictions. Yet, applications of language models, for instance, in eCommerce require checks and justifications by means of global reconstruction of their predictions, since the decisions based thereon can have large impacts or are even mandatory due to regulations such as the GDPR. To this end, we propose a novel XAI approach for global reconstructions of language model predictions for token-level classifications (e.g., aspect term detection) by means of linguistic rules based on NLP building blocks (e.g., part-of-speech). The approach is analyzed on different datasets of online consumer reviews and NLP tasks. Since our approach allows for different setups, we further are the first to analyze the trade-off between comprehensibility and fidelity of global reconstructions of language model predictions. With respect to this trade-off, we find that our approach indeed allows for balanced setups for global reconstructions of BERT’s predictions. Thus, our approach paves the way for a thorough understanding of language model predictions in text analytics. In practice, our approach can assist businesses in their decision-making and supports compliance with regulatory requirements. Collapse

Topics: explainable artificial intelligence laptop computer experience good electronic market artificial intelligence

Methods: BERT language model natural language processing machine learning product review

TRG-DAtt: The Target Relational Graph and Double Attention Network Based Sentiment Analysis and Prediction for Supporting Decision Making

2022 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Chen, Fan; Xia, Jiaoxiong; Gao, Honghao; Xu, Huahu; Wei, Wei

Abstract: The management of public opinion and the use of big data monitoring to accuratel ... Expand

Abstract: The management of public opinion and the use of big data monitoring to accurately judge and verify all kinds of information are valuable aspects in the enterprise management decision-making process. The sentiment analysis of reviews is a key decision-making tool for e-commerce development. Most existing review sentiment analysis methods involve sequential modeling but do not focus on the semantic relationships. However, Chinese semantics are different from English semantics in terms of the sentence structure. Irrelevant contextual words may be incorrectly identified as cues for sentiment prediction. The influence of the target words in reviews must be considered. Thus, this paper proposes the TRG-DAtt model for sentiment analysis based on target relational graph (TRG) and double attention network (DAtt) to analyze the emotional information to support decision making. First, dependency tree-based TRG is introduced to independently and fully mine the semantic relationships. We redefine and constrain the dependency and use it as the edges to connect the target and context words. Second, we design dependency graph attention network (DGAT) and interactive attention network (IAT) to form the DAtt and obtain the emotional features of the target words and reviews. DGAT models the dependency of the TRG by aggregating the semantic information. Next, the target emotional enhancement features obtained by the DGAT are input to the IAT. The influence of each target word on the review can be obtained through the interaction. Finally, the target emotional enhancement features are weighted by the impact factor to generate the review's emotional features. In this study, extensive experiments were conducted on the car and Meituan review data sets, which contain consumer reviews on cars and stores, respectively. The results demonstrate that the proposed model outperforms the existing models. Collapse

Topics: decision making

Methods: experiment computational algorithm BERT word embedding machine learning